1. Field of the Invention
The present invention relates generally to scanners and scanning methods, and more particularly to a method for generating a tonal response curve for a scanner.
2. Description of the Related Art
The way light is perceived by the human eye is very different from the way it is perceived by an image sensor, such as a photodiode, used in a scanner. Thus, in order to obtain an accurate facsimile of an original image from an image sensor, such as those used in scanners, multifunction printers, digital cameras, etc., the data captured by the sensor must be processed so that to human observation the original image and digital copy look the same.
It has been shown through experimental evidence that subjective brightness (intensity as perceived by the human visual system) is a logarithmic function of the light intensity incident on the eye. See, Gonzalez, Woods. Digital Image Processing Second Edition. Upper Saddle River, N.J.: Prentice Hall, 2002. Thus, in order to perceive a doubling in light intensity, the actual light incident on the eye must be increased exponentially. This is in stark contrast to the response of a photodiode which can be described by the equation ip=R×Popt, where ip is the reverse diode current induced by the incident light, R is the responsivity (measured in mA/mW), and Popt is the total light flux incident on the photodiode (measured in mW). Horenstein. Microelectronic Circuits and Devices Second Edition. Englewood Cliffs, N.J.: Prentice Hall, 1996. As is evident from the equation, in order to double the amount of current, the amount of incident light on the photodiode needs also only to be doubled.
Optical density is the base 10 Log of opacity, which is simply the ratio of incident light to transmitted light. Since the optical density scale is logarithmic, the human eye perceives it as linear. However, the image sensor is only sensitive to total incident light, so the output of the image sensor need to be corrected to make the image appear consistent with the original to human perception.
This correction may be achieved via a tonal response curve. The tonal response curve is determined before any image capture is performed in order to produce a good quality digital copy of the original image. Currently, this is manually done with a subset of machines which each utilize the same type of image sensor and several uniformly neutral target images with known optical properties. This one tonal response curve is then applied to every machine utilizing that image sensor type.
For example, a current process for determining a tonal response curve involves using a grayscale as the original image. The grayscale image is scanned with the image sensor and data from the image sensor is collected. The collected data is then compared to the known optical densities of the grayscale image. Thereafter, a tonal response curve is constructed based on the collected data and the comparison.
This process is carried out on a subset of machines which all use the same image sensor type, and the resulting tonal response curve (which will be averaged across the subset) is then applied to every machine that uses that image sensor. In other words, every machine having the same image sensor type has the same tonal response curve. What this means, of course, is that variations from machine to machine are not accounted for. Thus, it is not only possible, but probable that images from one machine will not look the same as images from another, and that either or both may not look like the original to a human observer.